What are Celeb Deepfakes?

General public was made aware of the deepfake technology in 2017, when an anonymous Reddit user posted fake adult videos featuring famous celebrities like Scarlett Johansson. These were not real-life footage but celebrity faces and adult videos were combined using deepfake to make the videos appear real. Over the years the technology has picked up where the algorithm does not require ample video footage, which is only available for public figures to create fake videos.

Learn how Fraud detection solution will help out in detecting false patterns.

How are DeepFakes Celeb Made?

Creation of Face-swap videos involve a series of steps. It starts with running thousands of face shots of two people through the encoder (AI algorithm). The encoder finds similarities and differences between the two and compresses the images to combine the common features in the two faces. 

Then a decoder is trained to recover the faces from the compressed images. A different decoder is used for each face. The final step involves feeding encoded images to the “wrong” decoder, for instance feeding the images of person A to the decoder trained on person B. The decoder uses the expression and orientation of person A to  reconstruct the face of person B. In order to produce a convincing video, the process needs to be repeated for each frame.

Best Celeb DeepFakes Images

Check out this collage of Chris pine morph into some shape shifting alien 🙂 Lol.

Best Celeb DeepFakes Videos

Artist Bill Posters in response to Facebook’s refusal to remove Nancy Pelosi’s video, posted a fake video of Mark Zuckerberg in which he can be seen claiming to use Facebook users.

This video, Better Call Trump: Money Laundering 101 is based on a scene from Breaking Bad, whereby introducing Trump as James Gill.

A deepfake video was uploaded by Mystere Giraffe in which Will Smith’s face was plastered on Cardi’s body. It looked very real and was shared by both the celebrities on their social media accounts.

Best Celeb DeepFakes FAQs:

How are celeb DeepFakes made for free?

People download apps like FakeApp generally and look at how-to tutorials online to train their computers to pull off face-swaps. Users in China can use easy solutions like Zao, whereas Carica is another app that creates deepfakes in a matter of seconds.

What software is used for DeepFakes?

Deep learning is a set of intelligent algorithms that can learn and make decisions on their own. DeepFake employs AI and Machine Learning as a service to create content. The method also involves training generative neural network architectures.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

Machine learning as a service allow companies to process large datasets more efficiently than humans. With NLP implementation, thousands of narratives can be created, where humans can only write a single piece at a time.

Best AI Story Generator Software

1) Grammarly

Grammarly is a great tool that helps create content that is concise, readable and error-free. It identifies and fixes writing mistakes in basic to complex material, depending on the plan. It finds problem areas and makes word & sentence structuring suggestions. It also allows you to add new words to the dictionary and receive reports on writing mistakes you make regularly. Premium plans start from $0 to $11.66 a month and business plan that can be shared with up to 100 people, costs $15/mo.

2) Textio

Textio is a good tool that enables augmentation of your writing to produce effective job listings. It scores writing skills and suggests the phrases that might be more apt for recruiting the most qualified applicants. It also reports on the key characteristics of your applicant pool, such as gender and diversity. Gmail and LinkedIn Recruiter integration allows you to write and post from anywhere, while also enabling you to share documents with other teams within your organisation.

The Textio Hire Package that facilitates job and email communication subscription ranges around $17-27k for a 12 month period. The final figure is based on the number of months, job postings and business size.

3) Articoolo

Articoolo is another great tool that is used by writers to create greater volumes of content in a more efficient manner. It helps find the best material, message and keywords to help you bring your project to life. While you pick the topic and length, it takes the material and utilizes NLP to rewrite it in order to produce unique material that can be used as a starting point. Moreover, it has a WordPress plug-in that can find images that go with the articles. The price is based on pay per use: 10 articles are $19,  50 articles are $75, 100 articles are $99. 30 articles per month can be subscribed for $29/mo, 100 articles per month at $49/mo and 250 articles per month at $99/mo.

4) ProWriting Aid

ProWriting Aid works as an online editor, writing and style guide - all in one. It identifies issues in your writing that are likely to go unnoticed such as overused or abstract words. The free version allows you to upload copies of your documents and receive up to 20 in-depth writing reports. These can be utilized to strengthen and bring clarity to your text.  ProWritingAid smoothly integrates with numerous other software, such as MS Word, GoogleDocs, Scrivener, Open Office, or Chrome. This means you can edit wherever you write.

5) Article Forge

Article Forge platform utilizes powerful algorithms that can automatically review existing articles similar to how humans do. The intelligent algorithms have the capability to conduct research and go through an infinite number of articles in order to write articles in their own words. It can also generate search engine optimized pieces. The tool allows for easy scheduling and automation of WordPress postings. You can pay $297 yearly or $47 monthly, plus it comes with a 30 days no risk and money back guarantee.

Key Difference Between Speech to Text and AI Generated Stories App

Speech to text software is a computer program that utilizes linguistic algorithms to understand auditory signals and converting it into words by using Unicode characters. In simple words, this  automatic speech recognition (ASR) software listens to audio and delivers precise and editable transcripts.

AI generated stories app is based on NLG, which allows generation of written narratives from data. This software is already being used for business intelligence dashboards, personalized email, data reports and more. Structured or raw data is processed by the software through the "conditional logic" to produce narratives that sound like human-generated content.

Folio3.Ai Offer Custom AI Story Generator Solution

Enable greater time savings and more control with our AI Story Generator Solution. Utilize Folio3’s AI story generator software to create unique, SEO optimized and information-densed content. Our Speech to text software also allows you to reword existing articles and add value. We have the expertise to generate custom article software with exceptional text generation abilities. We offer just the right coding expertise to help you utilize our text AI in your autoblogging projects. Our AI story generator can be used as a good source of inspiration and ideas.

FAQs:

Can AI write a story without plagiarism?

Most AI-generated stories are based on existing templates. These templates are semi-automatically generated sentences or stories that the machine uses according to the context that it is working in. These slots are then filled with relevant material to create an AI-generated story. Even though Predictive analytics has improved a lot over time, it still cannot generate human-like prose or produce fluid paraphrases. Due to this, AI stories often resemble works that already exist. Human intervention is therefore needed to make the AI-generated stories smoother and more original.

What is GPT2 for beginners?

GPT-2 stands for “Generative Pretrained Transformer 2”. Generative refers to the model’s automated predictive capabilities. This means that the model is trained to identify statistical features in a given data-set, raw or otherwise, to create more text. “Pretrained” refers to the enhancements made to the language model’s abilities to carry out specific tasks. This large and powerful model has become quite popular and we will see an upward trend moving forward. “Transformer” means that it is based on the transformer architecture, while “2” simply means this is not the first time GPT is being tried out.

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

What is Fairseq?

Fairseq PyTorch is an opensource machine learning library based on a sequence modeling toolkit. It allows the researchers to train custom models for fairseq summarization transformer, language, translation, and other generation tasks. It supports distributed training across multiple GPUs and machines. GitHub hosts its repository.

Fairseq Features in 2020

Fairseq provides researchers with smooth implementation of sequence to sequence models. It supports various models. Some of them include:

Convolutional Neural Networks (CNN)

Convolutional Neural Networks are a form of deep neural networks commonly used for visual imagery. They are useful in areas such as object detection, image recognition and other computer vision stuff. Fairseq is handy with the following:

1) Fairseq Language Modelling with Gated CNN

2) Classical Structured Prediction Losses

3) Hierarchical Neural Story Generation

4) Unsupervised Learning for Speech Recognition using predictive analytics solution

LightConv and DynamicConv Models

This model contains some pre-trained dataset and as well as the instructions on training the new model. It includes models without graphics library hence making it faster. You can quickly get the fairseq-preprocess datasets for languages such as English, Chinese, German, and French with fairseq-train paper.

Long Short-Term Memory (LSTM) Networks

LSTM is an artificial recurrent neural network (RNN) that are well-suited for classification and making predictions on time series data. It is convenient to use for unsegmented handwriting recognition, speech recognition, and anomaly detection in network traffic. Fairseq provides a practical approach to solve Attention-based Neural Machine Translation.

Transformer (self-attention) Networks

In place of CNN and RNN, many researchers prefer to use transformer networks. They implement encoder and decoder as self-attention networks to draw global dependencies between input and output. It works well in:

1) Scaling Neural Machine Fairseq Translation

2) Understanding Back-Translation

3) Mixture Models for Diverse Machine Translation

4) Input Representations for Neural Language Modeling

Non-autoregressive Transformers

Non-autoregressive Transformers or NAT removes the dependencies from the inputs of the decoder on the previous target token with fairseq bart. It helps to achieve:

1) Non-autoregressive Neural Machine Translation

2) Neural Sequence Modeling Iterative Refinement

3) Flexible Sequence Generation by Fairseq Insertion Transformer Model

4) Mask-Predict: Conditional Masked Language Models Parallel Decoding.

Apart from all these supported models and techniques by Fairseq, it also has other advantages. You can do multi-GPU training either on one machine or multiple machines. One can quickly implement them on both CPU and GPU with search algorithms. With its mixed-precision training, you can train models while consuming lesser GPU memory. It is extensible and makes registering of new models, tasks, and optimizers convenient.

FairSeq GitHub

The GitHub repository of Fairseq is at this link. It has 1128 commits with eight branches and 11 releases. Over six thousand people have starred it while 1.7k forked it. It has about 132 contributors with an active community backing it up.

How to Use FairSeq - Installation Requirements and Prerequisite

1) As Fairseq is an ML library in python, so you need python with version 3.6 or onwards.

2) PyTorch is also necessary before proceeding with Fairseq. You will require version 1.2.0 or onwards.

3) For training models, you will need an NVIDIA GPU. For better and efficient results, use NCCL.

4) Install NVIDIA’s apex library for faster training with the following two commands. 

--cuda_ext 

--deprecated_fused_adam

5) After fulfilling all the requirements, install Fairseq. You can either clone it by ‘git clone https://github.com/pytorch/fairseq’ or use the command ‘pip install fairseq.’

After successfully installing the fairseq, you can view its documentation here to get started. You even get pre-trained models and datasets with which you can get familiarization with the new library. Each pre-trained model has its READMEs as well for your convenience.

How to Install Fairseq - Interactive Installation Guide

There are a few simple steps to get started with fairseq. Follow the sequence:

1) First, you need python installed on your machine. Make sure its version is either 3.6 or higher. You can get python for your computer here.

2) After getting python, you need PyTorch. The underlying technology behind fairseq is PyTorch. You need version 1.2.0 or higher. To get PyTorch, you can clone it by the command ‘git clone https://github.com/pytorch/pytorch.git.’ You can install it from Anaconda or Chocolatey based installed. Here is the documentation.

3) Get fairseq by typing the following commands on the terminal.
git clone https://github.com/pytorch/fairseq.git

cd fairseq

pip install -r requirements.txt

python setup.py build develop

Download pre-trained models and get acquainted with the syntax.

Start working on new projects and models.

Fairseq Machine Translation Youtube

FAQs

• fairseq.modules.AdaptiveInput (AdaptiveInput is the module name)
• fairseq.modules.AdaptiveSoftmax (AdaptiveSoftmax is the module name)

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

Please feel free to reach out to us, if you have any questions. In case you need any help with development, installation, integration, up-gradation and customization of your Business Solutions. We have expertise in Machine learning solutions, Cognitive Services, Predictive learning, CNN, HOG and NLP.

Connect with us for more information at [email protected]

Artificial intelligence is an emerging technology. It is impacting the interactive activities of users through the internet. It possesses the ability to change the way that humans interact, not only with the digital and electronic world but also with each other. It is basically a form of technology with human-like intelligence that can learn, perceive, plan, and process languages. There are two categories of AI, which include “narrow AI” or “general AI.” Narrow AI is domain-specific that we interact with today. By domain-specific, we mean, for instance, language translation. General AI is theoretical and not domain-specific. Just to remain in the context, we would focus only on narrow AI. It is a machine language – a field of computer science for the development of new algorithms and models. Conversely, we can say machine language is an application of Artificial Intelligence. The list of machine languages is vast, with several different categories. Here we are going to discuss R – a programming language in detail. The language falls in the category of Array languages. So, what is R? R comprises many properties. It is a machine language that provides an open-source environment for statistical programming and visualizing. There are many R machine learning packages, which we will be discussing in detail. You can find seamlessly designed AI solutions such as R learning packages in the market by machine learning solution companies such as Folio3.

21 Best R Machine Learning Packages in 2020

The most commonly asked question by potential data scientists is, “What is the best programming language for Machine Learning as a service?” The answer to this question always ends up in a debate about whether to choose R, Python, or MATLAB for Machine Learning. Choosing the programming language for machine learning depends on the requirements of a data problem, the preferences of the data scientist. According to the Kaggle survey, open-source R is the preeminent choice among data specialists who want to understand and explore data by using statistical methods and graphs. There are many R machine learning packages and their advanced implementations for the top machine learning algorithms. Every data specialist must be familiar about them to explore, model, and create a prototype of the given data. Since R is an open-source language, so people can approach it from anywhere in the world. From data collection to reproducible research, you can find a Black Box written by someone else that you can use in your program directly. The Black Box is nothing but a Package in R, which is a collection of pre-written reusable codes. Here are the few basic R learning machine packages:

1) CARAT

The CARAT package denotes as Classification and Regression Training. The purpose of this package is to integrate training and model prediction. It allows data specialists to run several different algorithms for a given problem. It also facilitates in investigating the ideal parameters for an algorithm with measured experiments.

CARAT Features And USP: The grid search method of this package explores parameters with the help of a combination of various methods to evaluate the performance of a given model. After having a look at all the trial combinations, the grid search method discovers the combination that gives the best results. CARAT package is among the best machine learning packages in R. After installing the CART package; a developer can run names (getModelInfo()) to see that 217 possible methods require a single package to operate or run. To build any predictive model, CARAT uses train() function; The syntax of train function is train(formula, data, method). CARAT Documentation Download: CARET package is not only for building models, but it also takes care of splitting your data into test and train, alteration, etc.

2) Random Forest

The concept behind its name is to “combine multiple trees to build your forest.” A Random Forest algorithm is the most widely used algorithm in Machine Learning. Its application includes the creation of a large number of decision trees, and then each observation is entered into the decision tree. The common output attained for a maximum of the observations is measured as the final output.

In other words, it takes random samples. Observations are arranged into the decision tree. While using the randomForest algorithm, data specialists have to confirm that the variables must be numeric or factors. Factors cannot have more than 32 levels when applying randomForest. This package allows for solving regression and classification tasks. Training missing values and outliers is one of its many applications. The syntax of this function is: randomForest(formula=, data=)

3) E1071

The name of this package seems a junk value, but this is not the case. It is a very significant package in the R machine learning package. It has very specified functions for implementing Naïve Bayes (conditional probability), SVM, Fourier Transforms, Bagged Clustering, Fuzzy Clustering, etc. E1071 R package implemented the first R interface for SVM. E1071 Documentation Download: The easy way to understand its concept is, let's suppose if a data specialist is trying to find out what is the probability that a person who buys an iPhone 8 also buys an iPhone 8 Case. It is a type of investigation which depends on conditional probability, so data scientists use an e1071 R package that has specialized functions for implementing the Naive Bayes Classifier.

Data scientists use Support Vector Machines (SVM) when they have a dataset that is impossible to separate in the given dimensions, and there is a need to promote that data to higher dimensions to classify or regress it. The syntax for SVM is: SVM(Species ~Sepal.Length + Sepal.Width, data=iris)

4) RPart

Rpart stands for recursive partitioning and regression training. Classification and regression are the two primary purposes of this package. It involves the two-stage procedure. The output model is then represented in the form of binary trees. The general way to plot any function using the Rpart package is to call plot() function. The results might not be appealing by just applying the basic plot() function, so there is a substitute that is the PRP() function. It is an influential and flexible function in rpart.plot package. It is frequently denoted as the authentic Swiss army knife for plotting regression trees.

rpart() function helps to create a relationship between dependent and independent variables so that a business can understand the difference in the dependent variables based on the independent variables. The syntax is: rpart(formula, data=, method=,control=) This formula implies as follows: Data implies the name of the dataset. The method implies the objective. Control implies the system requirement.

5) KernLab

KernLab is a package for SVM, kernel feature analysis, ranking algorithm, dot product primitives, Gaussian process, and a spectral clustering algorithm. KernLab’s most common use is for SVM implementations. It comes in use when it is difficult to solve clustering, classification, and regression problems.  It has several kernel functions that include tanhdot (hyperbolic tangent kernel Function), polydot (polynomial kernel function), laplacedot (laplacian kernel function) and many more to perform pattern recognition problems. KernLab has its predefined kernels, but the user has the flexibility to create and use their kernel functions.

6) Nnet

Data scientists use this package when there is a need to use an artificial neural network (ANN) as it is based on an understanding of how a human brain functions. It is one of the widely used and easy to implement a package of neural networks, but it is restricted to a single layer of nodes. According to several studies, more nodes are not required because they do not contribute to improving the performance of the model but rather increase the calculation time and complexity of the model. This package does not offer any particular set of methods for finding the number of nodes in the hidden layer. Thus, when data specialists apply nnet, it is most likely to recommend that they arrange it in such a way that a value falls between the number of input and output nodes.

The syntax for this package is: nnet(formula, data, size) To view the documentation of its function, go to this link: https://www.rdocumentation.org/packages/nnet/versions/7.3-12/topics/nnet

7) DPLYR

Dpylr is one of the most popular packages in the field of data science. It provides feasible, fast, and stable functions for data handling. This package contains some set of verbs as functions like mutate(), select(), filter(), and arrange(). The following code is used to install this package: install.packages(“dplyr”) To load this package following syntax is used: library(dplyr)

8) GGPlot2

Ggplot2 is another package for data science. It is the most sophisticated and artistic graphic framework among R packages. The syntax for the installation of this data science package is  install.packages(“ggplot2”)

9) Word Cloud

Word cloud, as the name indicates, it consists of thousands of words in a single image. Conversely, we can say that it is a visualization of text data. One great example is speech to text software. It is one of the best machine learning packages in R that creates a representation of words. Data specialists can customize the Worldcloud according to his choice. Like he can place the words randomly in his desired position, or he can place the same kind of words together, etc.  In the R machine learning package, two types of libraries are there to make Worldcloud that are Worldcloud and Worldcloud2. Here we will present the syntax of Worldcloud2. The installation syntax for Worldcloud2 is:

1. require(devtools)
2. install_github(“chiffon/wordcloud2”)

Or you can write it directly as: a library(wordcloud2).

10) Tidyr

It is another popular R package for data science. The function of the tidyr package for data is science is to tidy up the data. It is done by placing variables in the column, observation in the row, and the value in the cell. This package is used to define the standard way of organizing data.  For installation syntax, use this code: install.packages(“tidyr”) For loading the package, use this code: library(tidyr)

11) Shiny

Shiny is an R package, the use of which stretches to web application frameworks for data science. It provides an effortless solution for building up web applications. There are two options, the developer can install the software on every client system, or he can host a webpage. In addition, the developer can create dashboards or can implant them in R Markdown documents. Furthermore, Shiny apps can be used with various scripting languages like HTML widgets, CSS themes, and JavaScript actions. Conversely, this package is considered as a combination of the computational power of R with the interactivity of the modern web.

12) Tm

tm is a machine learning package of R that gives a framework for solving text mining tasks. Text mining is an evolving application of natural language processing these days.  Text mining application involves sentiment analysis or news classification. There are several jobs for a developer that he has to perform in this package, such as eliminating unwanted and unrelated words, eliminating punctuation signs and ending words, etc. This package has many adaptable functions to provide ease. Some of them are as follows:

• removeNumbers(): to remove Numbers from the given text document.
• weightTfIdf(): for term Frequency and reverse document frequency.
• tm_reduce(): to combine transformations.
• removePunctuation() to remove punctuation signs from the given text document.

13) MICE Package

MICE package refers to Multivariate Imputation via Chained Sequences. One of the many uses of this package includes imputing the missing values. It is a common problem that developers usually face. Generally, when a developer faces a problem of missing values, he applies basic imputations like replacing with 0, mean or mode, etc. These solutions are not flexible and may result in possible data inconsistency. Therefore MICE package facilitates developers to impute missing values with the help of multiple techniques according to the type of given data. This package consists of various functions like inspecting missing data patterns, diagnosis of the quality of the imputed value, analyses completed dataset, store and export imputed data in different structures, etc.  For the package documentation, click on this link https://www.rdocumentation.org/packages/mice/versions/3.6.0/topics/mice

14) iGraph

igraph is one of the top machine learning R packages for data science used for network analysis. It is a combination of powerful, professional, accessible, and portable network analysis tools. Moreover, it is an open-source and free package and can be programmed on Python, C/C++, and Mathematica. This package consists of numerous functions that help to produce random and regular graphs, visualization of a graph, etc in computer vision. By using this R package, developers can work on their huge graphs.  There are some specific requirements to use this package for Linux. It needs a C and a C++ compiler. The installation syntax for this package is: install.packages(“igraph”) The loading syntax for this package is: library(igraph)

15) ROCR

This data science R package helps in visualizing the performance of scoring classifiers. This package is flexible and feasible. It requires only three commands and default values for optional parameters. This package facilitates in developing cutoff-parameterized 2D performance curves. This package comprises of various functions such as:  prediction() - used to create prediction objects performance() - used to create performance objects, etc. For package documentation, view this link https://www.rdocumentation.org/packages/ROCR/versions/1.0-7

16) Data Explorer

The DataExplorer package is among the widely used and top machine learning R package for data science. This package serves in the field of exploratory data analysis (EDA), which is one of the predictive analytics tasks. As the name implies, in this package, the data analyst has to be more attentive in data analysis. It is not easy to handle data manually. For this purpose, the DataExplorer package provides automation for data exploration. This package helps in scanning and analyzing every variable to visualize them. It is beneficial for a huge dataset. The data analysis can easily obtain the hidden knowledge of data. You can use this mentioned code to install the package from CPAN: install.packages(“DataExplorer”) To load this package, the following code is needed: library(DataExplorer)

17) MLR

The mlr package of machine learning is one of the most amazing packages. It provides encryption to various machine learning tasks. It can perform several tasks with the help of only a single package rather than to use three packages for three different tasks. mlr package provides coherence for various classification and regression techniques. The techniques involve machine-readable parameter descriptions, clustering, generic re-sampling, filtering, and feature extraction, etc. It can also perform parallel operations. For installation, this code is used: to install.packages(“mlr”) The code for loading this package is; library(mlr)

18) Arules

The arules package arules refers to Mining association rules and Frequent Itemsets. It is also a widely used R machine learning package. This package helps in performing several operations that include representation and transaction analysis of data and patterns and data manipulation. It also provides the C implementations of Apriori and Eclat association mining algorithms.

19) Mboost

mboost is another R machine learning package for data science. It is a package that depends majorly on model boosting. It has an operative gradient descent algorithm for enhancing general risk functions. It uses regression trees or component-wise least squares estimates. Also, it provides an interaction model to potentially high-dimensional data

20) Party

The applications of Party package in R machine learning extends to recursive partitioning. This package imitates the continuous development of collective methods. Party is another package like a randomForest package to make a decision tree, which is based on the Conditional Inference algorithm. The main function of this package is ctree() that reduces the training time and bias. The syntax code for ctree() is: ctree(formula,data)

Folio3 Is Your Best Custom Machine Learning As a Service Partner

Folio3 is a leading software company, providing customized solutions in machine learning. With the prolonged experience of decades, Folio3 has helped many partner companies from various industries. It has the expertise of AI and machine learning in these following processes.

• Product Conceptualization
• Predictive Engineering and maintenance analysis
• Design and Automation
• Data Acquisition and analytics
• Product planning and commissioning
• Utilizing operational data to improve processes

Folio3 can also alter and customize the development process according to your requirements. Their professional and talented team follows the predefined and standard process based on best practices :

• Submission of hypothesis
• Scope and feasibility of the project defined Delivery of a Proof of Concept
• Algorithm development with regular touchpoints Final delivery and live deployment

Folio3 is capable of understanding customer needs and hence able to create machine learning solutions accordingly. Artificial Intelligence and machine learning help to develop great projects, but it is also important to provide the best user experience. So, Folio3 supports regular interaction with customers and related stakeholders. Folio3 has delivered its efficient services in many industries. Here we are mentioning nine among them and their related use cases:

1) Banking Sector – ATM Cash Forecasting 

Folio3 has provided its assistance to the multinational and large commercial banks of Pakistan. It served them with the service of deep learning for ATM cash Forecasting. It helps banks in avoiding both out of cash and overstock situations at their ATMs across the world. It also provides automated analysis of previous transactions for predicting the required amount of money for individual ATMs.

2) Health Sector - Breast Cancer HER2 Subtype Identification

Folio3 provided the solution of computer vision for Breast Cancer HER2 subtype identification to one of the most popular universities of Pakistan, Dow University of Health Sciences. The solution provides an automated pipeline for cell segmentation and spot counting from a Computer Vision-based diagnostic-aid for the Fluorescent In-Situ Hybridization test. For this reason, Folio3 developed a computer-supported assistance system that allowed specialists to perform the test efficiently and accurately and also allows them to digitize and store the images for future practice.

3) Transportation Sector – Road traffic Analysis 

It was a propriety service of deep learning by Folio3 for the transportation sector. They built an AI-powered Road & Safety solution, which allowed the analysis of road and traffic situations by making use of advanced deep learning. It can precisely distinguish between various types of vehicles and perform a total count.

4) Trading Sector  - Completion Time Estimation 

Folio3 has provided its services to the largest bookseller company of the United States in the area of machine learning. The solution has enabled the customer’s Digital Marketing Team to considerably improve the effective delivery through accurate schedules and outcomes of marketing campaigns while meeting the weekly related deadlines.

5) Medical Diagnosis – Thalassemia Identification

Folio3 again served the Dow University of Health Sciences for Thalassemia Identification, a project of image analysis and ai image processing. The solution provides automated analysis of gel electrophoresis images to predict thalassemia and test for mutant gene expression with fine-granularity medical image analysis.

6) Food Sector - Automated Authentication for Drive-Thrus 

Folio3 served the product development company of California named “Dashcode.” They created the automated authentication for a drive-thru, which is a project of deep learning.  The solution offers multi-modal automated authentication for drive-thrus through fine-grained car’s make/model classification and person identification using deep learning.

7) Speech to Text App – Converse Smartly

It was a proprietary application by Folio3. It allows you to make your conversations smart, intelligent, and productive with the use of machine learning, artificial intelligence, and Natural Language Processing (NLP).

8) Technology Sector – Facial Recognition system 

It was also a propriety product built by Folio3. This solution offers a highly accurate facial recognition system that provides real-time results based on Histogram of Oriented Gradients (HOG) and Convolutional Neural Network (CNN). 

9) IT sector – Customer Churn Prediction 

Folio 3 has served as the leading tech company of Pakistan with the facility of customer churn prediction, a predictive modeling solution. The Customer Churn Prediction offers a data-driven insight for businesses, helping them to recognize potentially unsatisfied and inactive customers in real-time with business process validation. For detailed solutions and services, Check these urls: Terrain Mapping Livestock Management Amazon Transcribe Service Google Speech to Text Service IBM Watson Consulting Service Azure Machine Learning Service Big Data Solution Robotic Process Automation Solution Edge Analytics Services Fraud Detection Solution

Top R Machine Learning Packages Conclusion:

All R machine learning packages are the eminent choice based on their features and functions, and every package best fits according to the given data requirements. There are some default values related to every package in R. Prior to the implementation of an algorithm, a data specialist or a developer must know about its numerous options available. By entering default values will give some outcomes, but that outcome is not likely to be accurate or optimized. In other words, by using the definite functions of R, one can develop an efficient machine learning or data science model. Hence, the R machine learning package is an amazing open-source RStudio tool, providing everyone to avail of the opportunity to use.  If you find our blog useful and informative, please share it with your friends and family. If you have any further suggestions or queries, please leave a comment in our comment section. FAQs: Is r used for machine learning and iot as well? Yes, R can be used for both because R itself a machine language applicable in machine learning for data science tasks. As we know that Artificial Intelligence is associated with IoT, and we refer narrow AI to machine learning that includes R. Hence there is a strong relation of R machine learning in IoT applications. Advantages of r packages in machine learning? There are several advantages of R packages in machine learning. Some of them include Open Source – This means everyone has the chance to avail of these packages without the need for license and registration process. Ideal support for data scattering – It allows scattered data to transform in a structured and organized form. Variety of packages – there is a variety of packages for different data issues. Provides quality plotting and graphing – The packages like ggplot2 provides visual tools for plotting and graphing the data. Highly compatible – It is highly compatible and can be combined with many programming languages like C, C++, Python, and Java. Platform independent - It is independent of any platform that means it is cross-platform machine language that can be compatible with Windows, Linux, Mac. Machine learning operations – It supports many machine learning operations like classification, regression, and development of artificial neural networks. Constantly growing – It is progressing continuously with the addition of new features that provide developers to work efficiently without any delay. Which packages in r provides machine learning functionality for beginners? Here is the categorized list of R packages that are useful for beginners:
To manipulate data:

• dplyr
•  tidyr

To visualize data:

• ggplot2

To model data:

• CARAT

 To report results:

• Shiny

Many other R machine learning packages fall in these categories; here, we mentioned the most feasible and popular packages useful for beginners.

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